Real-time underwater video feed enhancement for autonomous underwater vehicles (AUV)

Yusuf Hasan; Athar Ali

doi:10.1117/12.3013661

7 June 2024 Real-time underwater video feed enhancement for autonomous underwater vehicles (AUV)

Yusuf Hasan, Athar Ali

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330U (2024) https://doi.org/10.1117/12.3013661
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

In underwater exploration, Autonomous Underwater Vehicles (AUVs) face challenges due to the adverse effects of the aquatic environment on optical sensors, resulting in sub-optimal data acquisition. To overcome this, we propose a novel solution utilizing a Generative Adversarial Network (GAN) model. Rooted in the U-Net architecture, our model processes low-quality AUV camera feed, generating enhanced representations of the underwater scene. The discriminator focuses on evaluating current image patches, capturing high-frequency properties with fewer parameters, achieving a 15% improvement in model accuracy. This approach facilitates realtime preprocessing in visually-guided underwater robot autonomy pipelines, overcoming challenges associated with underwater visibility

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yusuf Hasan and Athar Ali "Real-time underwater video feed enhancement for autonomous underwater vehicles (AUV)", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330U (7 June 2024); https://doi.org/10.1117/12.3013661

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available